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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(2): 188-193 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.410

A Survey of Different SPIT Mitigation Methods and a Presentation of a Comprehensive SPIT Detection Framework

Mohammad Hossein Yaghmaee Moghaddam, Mina Amanian, Farideh Barghi, and Hossein Khosravi Roshkhari
Abstract—VoIP is a promising technology for voice transmission on IP-based networks and it has many advantages over PSTN. One of the most important threats in these networks is unsolicited bulk calls, known as SPIT (Spam over Internet Telephony). Our purpose in this paper is doing a deep research into this topic and presenting a new anti-SPIT mechanism.
   In order to detect SPIT efficiently we need to extract some features which help us in categorizing the incoming calls. In this paper we propose an approach based on extraction of important features that contain all aspects of call, hence it can detect SPIT efficiently in an acceptable time. In this paper, eight features which are directly extracted from the SIP header are applied in the detection process.
   The simulation results show that the proposed framework is a comprehensive and efficient solution which provides acceptable true-positive and false-negative values.

Index Terms—PSTN, SPIT, SIP, VoIP, Caller, Callee.

F. Mohammad Hossein Yaghmaee Moghaddam is with the Dept. of engineering Ferdowsi University, Mashhad, Iran (e-mail: yaghmaee@ieee.org).
S. Mina Amanian is with the Dept. of Computer Engineering Imam Reza International University, Mashhad, Iran (e-mail:minaamanian@gmail.com, farideh.1367@gmail.com).
F. Hossein Khosravi Roshkhari is with the Dept. of Engineering Ferdowsi University, Mashhad, Iran (e-mail: Hos.khosravi.r@gmail.com).

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Cite: Mohammad Hossein Yaghmaee Moghaddam, Mina Amanian, Farideh Barghi, and Hossein Khosravi Roshkhari, "A Survey of Different SPIT Mitigation Methods and a Presentation of a Comprehensive SPIT Detection Framework," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 188-193, 2014.

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